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requirements.txt

@@ -1,3 +1,3 @@
 torch
 transformers
-https://gradio-main-build.s3.amazonaws.com/8edc91ff4b45981ad3e96288b404f9143115b178/gradio-3.10.1-py3-none-any.whl
+https://gradio-main-build.s3.amazonaws.com/4f24e146dac42a85de708d147f488fdf418d4a7f/gradio-3.10.1-py3-none-any.whl

run.ipynb

@@ -0,0 +1 @@
+{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: blocks_speech_text_sentiment"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio torch transformers"]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["from transformers import pipeline\n", "\n", "import gradio as gr\n", "\n", "asr = pipeline(\"automatic-speech-recognition\", \"facebook/wav2vec2-base-960h\")\n", "classifier = pipeline(\"text-classification\")\n", "\n", "\n", "def speech_to_text(speech):\n", "    text = asr(speech)[\"text\"]\n", "    return text\n", "\n", "\n", "def text_to_sentiment(text):\n", "    return classifier(text)[0][\"label\"]\n", "\n", "\n", "demo = gr.Blocks()\n", "\n", "with demo:\n", "    audio_file = gr.Audio(type=\"filepath\")\n", "    text = gr.Textbox()\n", "    label = gr.Label()\n", "\n", "    b1 = gr.Button(\"Recognize Speech\")\n", "    b2 = gr.Button(\"Classify Sentiment\")\n", "\n", "    b1.click(speech_to_text, inputs=audio_file, outputs=text)\n", "    b2.click(text_to_sentiment, inputs=text, outputs=label)\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}